





"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import (
          TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaubertConfig,
          TFFlaubertForMultipleChoice,
          TFFlaubertForQuestionAnsweringSimple,
          TFFlaubertForSequenceClassification,
          TFFlaubertForTokenClassification,
          TFFlaubertModel,
          TFFlaubertWithLMHeadModel,
      )







class       __a  :



     def __init__(			self   ,	a__   ,	):
            _lowerCamelCase             =   parent
            _lowerCamelCase             =   13
            _lowerCamelCase             =   7
            _lowerCamelCase             =   True
            _lowerCamelCase             =   True
            _lowerCamelCase             =   True
            _lowerCamelCase             =   True
            _lowerCamelCase             =   True
            _lowerCamelCase             =   False
            _lowerCamelCase             =   False
            _lowerCamelCase             =   False
            _lowerCamelCase             =   2
            _lowerCamelCase             =   99
            _lowerCamelCase             =   0
            _lowerCamelCase             =   32
            _lowerCamelCase             =   2
            _lowerCamelCase             =   4
            _lowerCamelCase             =   0.1
            _lowerCamelCase             =   0.1
            _lowerCamelCase             =   5_12
            _lowerCamelCase             =   16
            _lowerCamelCase             =   2
            _lowerCamelCase             =   0.02
            _lowerCamelCase             =   3
            _lowerCamelCase             =   4
            _lowerCamelCase             =   'last'
            _lowerCamelCase             =   True
            _lowerCamelCase             =   None
            _lowerCamelCase             =   0



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size  )
            _lowerCamelCase             =   random_attention_mask([self.batch_size, self.seq_length]   ,	dtype=tf.floataa  )

            _lowerCamelCase             =   None
            if self.use_input_lengths:
                   _lowerCamelCase             =   (
                       ids_tensor([self.batch_size]   ,	vocab_size=2  ) + self.seq_length - 2
                   )  # small variation of seq_length

            _lowerCamelCase             =   None
            if self.use_token_type_ids:
                   _lowerCamelCase             =   ids_tensor([self.batch_size, self.seq_length]   ,	self.n_langs  )

            _lowerCamelCase             =   None
            _lowerCamelCase             =   None
            _lowerCamelCase             =   None
            if self.use_labels:
                   _lowerCamelCase             =   ids_tensor([self.batch_size]   ,	self.type_sequence_label_size  )
                   _lowerCamelCase             =   ids_tensor([self.batch_size, self.seq_length]   ,	self.num_labels  )
                   _lowerCamelCase             =   ids_tensor([self.batch_size]   ,	2   ,	dtype=tf.floataa  )
                   _lowerCamelCase             =   ids_tensor([self.batch_size]   ,	self.num_choices  )

            _lowerCamelCase             =   FlaubertConfig(
                vocab_size=self.vocab_size   ,	n_special=self.n_special   ,	emb_dim=self.hidden_size   ,	n_layers=self.num_hidden_layers   ,	n_heads=self.num_attention_heads   ,	dropout=self.hidden_dropout_prob   ,	attention_dropout=self.attention_probs_dropout_prob   ,	gelu_activation=self.gelu_activation   ,	sinusoidal_embeddings=self.sinusoidal_embeddings   ,	asm=self.asm   ,	causal=self.causal   ,	n_langs=self.n_langs   ,	max_position_embeddings=self.max_position_embeddings   ,	initializer_range=self.initializer_range   ,	summary_type=self.summary_type   ,	use_proj=self.use_proj   ,	bos_token_id=self.bos_token_id   ,	)

            return (
                config,
                input_ids,
                token_type_ids,
                input_lengths,
                sequence_labels,
                token_labels,
                is_impossible_labels,
                choice_labels,
                input_mask,
            )



     def 		snake_case_     (			self   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	):
            _lowerCamelCase             =   TFFlaubertModel(config=__snake_case  )
            _lowerCamelCase             =   {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
            _lowerCamelCase             =   model(__snake_case  )

            _lowerCamelCase             =   [input_ids, input_mask]
            _lowerCamelCase             =   model(__snake_case  )
            self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)  )



     def 		snake_case_     (			self   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	):
            _lowerCamelCase             =   TFFlaubertWithLMHeadModel(__snake_case  )

            _lowerCamelCase             =   {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
            _lowerCamelCase             =   model(__snake_case  )

            self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.seq_length, self.vocab_size)  )



     def 		snake_case_     (			self   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	):
            _lowerCamelCase             =   TFFlaubertForQuestionAnsweringSimple(__snake_case  )

            _lowerCamelCase             =   {'input_ids': input_ids, 'lengths': input_lengths}

            _lowerCamelCase             =   model(__snake_case  )

            self.parent.assertEqual(result.start_logits.shape   ,	(self.batch_size, self.seq_length)  )
            self.parent.assertEqual(result.end_logits.shape   ,	(self.batch_size, self.seq_length)  )



     def 		snake_case_     (			self   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	):
            _lowerCamelCase             =   TFFlaubertForSequenceClassification(__snake_case  )

            _lowerCamelCase             =   {'input_ids': input_ids, 'lengths': input_lengths}

            _lowerCamelCase             =   model(__snake_case  )

            self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.type_sequence_label_size)  )



     def 		snake_case_     (			self   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	):
            _lowerCamelCase             =   self.num_labels
            _lowerCamelCase             =   TFFlaubertForTokenClassification(config=__snake_case  )
            _lowerCamelCase             =   {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
            _lowerCamelCase             =   model(__snake_case  )
            self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.seq_length, self.num_labels)  )



     def 		snake_case_     (			self   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	):
            _lowerCamelCase             =   self.num_choices
            _lowerCamelCase             =   TFFlaubertForMultipleChoice(config=__snake_case  )
            _lowerCamelCase             =   tf.tile(tf.expand_dims(__snake_case   ,	1  )   ,	(1, self.num_choices, 1)  )
            _lowerCamelCase             =   tf.tile(tf.expand_dims(__snake_case   ,	1  )   ,	(1, self.num_choices, 1)  )
            _lowerCamelCase             =   tf.tile(tf.expand_dims(__snake_case   ,	1  )   ,	(1, self.num_choices, 1)  )
            _lowerCamelCase             =   {
                'input_ids': multiple_choice_inputs_ids,
                'attention_mask': multiple_choice_input_mask,
                'token_type_ids': multiple_choice_token_type_ids,
            }
            _lowerCamelCase             =   model(__snake_case  )
            self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_choices)  )



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   self.prepare_config_and_inputs()
            (
                _lowerCamelCase      
            )             =   config_and_inputs
            _lowerCamelCase             =   {
                'input_ids': input_ids,
                'token_type_ids': token_type_ids,
                'langs': token_type_ids,
                'lengths': input_lengths,
            }
            return config, inputs_dict







@require_tf
class       __a  (    lowerCamelCase__      ,		lowerCamelCase__      ,		unittest.TestCase						):
     SCREAMING_SNAKE_CASE__					: str									=     (
         (
             TFFlaubertModel,
             TFFlaubertWithLMHeadModel,
             TFFlaubertForSequenceClassification,
             TFFlaubertForQuestionAnsweringSimple,
             TFFlaubertForTokenClassification,
             TFFlaubertForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE__					: Any									=     (
         (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
     SCREAMING_SNAKE_CASE__					: Optional[Any]									=     (
         {
             "feature-extraction": TFFlaubertModel,
             "fill-mask": TFFlaubertWithLMHeadModel,
             "question-answering": TFFlaubertForQuestionAnsweringSimple,
             "text-classification": TFFlaubertForSequenceClassification,
             "token-classification": TFFlaubertForTokenClassification,
             "zero-shot": TFFlaubertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     SCREAMING_SNAKE_CASE__					: Any									=     False
     SCREAMING_SNAKE_CASE__					: List[str]									=     False



     def 		snake_case_     (			self   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__  ):
            if (
                pipeline_test_casse_name == "QAPipelineTests"
                and tokenizer_name is not None
                and not tokenizer_name.endswith('Fast'  )
            ):
                   # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
                   # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
                   # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
                   return True

            return False



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   TFFlaubertModelTester(self  )
            _lowerCamelCase             =   ConfigTester(self   ,	config_class=__snake_case   ,	emb_dim=37  )



     def 		snake_case_     (			self  ):
            self.config_tester.run_common_tests()



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_model(*__snake_case  )



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_lm_head(*__snake_case  )



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_qa(*__snake_case  )



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_sequence_classif(*__snake_case  )



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_for_token_classification(*__snake_case  )



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_for_multiple_choice(*__snake_case  )



     @slow
     def 		snake_case_     (			self  ):
            for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _lowerCamelCase             =   TFFlaubertModel.from_pretrained(__snake_case  )
                   self.assertIsNotNone(__snake_case  )







@require_tf
@require_sentencepiece
@require_tokenizers
class       __a  (    unittest.TestCase						):



     @slow
     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   TFFlaubertModel.from_pretrained('jplu/tf-flaubert-small-cased'  )

            _lowerCamelCase             =   tf.convert_to_tensor(
                [[0, 1_58, 7_35, 25_92, 14_24, 67_27, 82, 1]]   ,	dtype=tf.intaa   ,	)  # "J'aime flaubert !"

            _lowerCamelCase             =   model(__snake_case  )[0]
            _lowerCamelCase             =   tf.TensorShape((1, 8, 5_12)  )
            self.assertEqual(output.shape   ,	__snake_case  )
            # compare the actual values for a slice.
            _lowerCamelCase             =   tf.convert_to_tensor(
                [
                    [
                        [-1.8768773, -1.566555, 0.27072418],
                        [-1.6920038, -0.5873505, 1.9329599],
                        [-2.9563985, -1.6993835, 1.7972052],
                    ]
                ]   ,	dtype=tf.floataa   ,	)

            self.assertTrue(np.allclose(output[:, :3, :3].numpy()   ,	expected_slice.numpy()   ,	atol=1e-4  )  )











'''simple docstring'''






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCAmelCase:   Optional[Any]					=		logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')

lowerCAmelCase:   Optional[int]					=		list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowerCAmelCase:   int					=		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class 				a__:
    lowercase__					=       field(
        default="""cifar10"""						,					metadata={"""help""": """Name of a dataset from the datasets package"""}      )
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}      )
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={"""help""": """The column name of the images in the files. If not set, will try to use 'image' or 'img'."""}						,					)
    lowercase__					=       field(default=lowerCamelCase__						,					metadata={"""help""": """A folder containing the training data."""}      )
    lowercase__					=       field(default=lowerCamelCase__						,					metadata={"""help""": """A folder containing the validation data."""}      )
    lowercase__					=       field(
        default=0.15						,					metadata={"""help""": """Percent to split off of train for validation."""}      )
    lowercase__					=       field(default=32						,					metadata={"""help""": """The size of the square patches to use for masking."""}      )
    lowercase__					=       field(
        default=0.6						,					metadata={"""help""": """Percentage of patches to mask."""}						,					)
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of training examples to this """
                """value if set."""
            )
        }						,					)
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                """value if set."""
            )
        }						,					)







    def      lowercase_			( self					:    List[Any]      ):
      a		:   Any	  =	{}
      if self.train_dir is not None:
        a		:   Dict	  =	self.train_dir
      if self.validation_dir is not None:
        a		:   Union[str, Any]	  =	self.validation_dir
      a		:   Any	  =	data_files if data_files else None

@dataclass
class 				a__:
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={
            """help""": (
                """The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a """
                """checkpoint identifier on the hub. """
                """Don't set if you want to train a model from scratch."""
            )
        }						,					)
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(lowerCamelCase__      )}						,					)
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}      )
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={
            """help""": (
                """Override some existing default config settings when a model is trained from scratch. Example: """
                """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
            )
        }						,					)
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""}						,					)
    lowercase__					=       field(
        default="""main"""						,					metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}						,					)
    lowercase__					=       field(default=lowerCamelCase__						,					metadata={"""help""": """Name or path of preprocessor config."""}      )
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={
            """help""": (
                """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                """with private models)."""
            )
        }						,					)
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={
            """help""": (
                """The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
            )
        }						,					)
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={
            """help""": (
                """The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
            )
        }						,					)
    lowercase__					=       field(
        default=lowerCamelCase__						,					metadata={"""help""": """Stride to use for the encoder."""}						,					)

class 				a__:







    def __init__( self					:    List[str]		,     __snake_case					:    int=1_92		,     __snake_case					:    int=32		,     __snake_case					:    List[str]=4		,     __snake_case					:    Union[str, Any]=0.6      ):
      a		:   Any	  =	input_size
      a		:   Union[str, Any]	  =	mask_patch_size
      a		:   int	  =	model_patch_size
      a		:   Tuple	  =	mask_ratio

      if self.input_size % self.mask_patch_size != 0:
        raise ValueError('Input size must be divisible by mask patch size'      )
      if self.mask_patch_size % self.model_patch_size != 0:
        raise ValueError('Mask patch size must be divisible by model patch size'      )

      a		:   str	  =	self.input_size // self.mask_patch_size
      a		:   Union[str, Any]	  =	self.mask_patch_size // self.model_patch_size

      a		:   str	  =	self.rand_size**2
      a		:   Tuple	  =	int(np.ceil(self.token_count * self.mask_ratio      )      )







    def __call__( self					:    str      ):
      a		:   List[str]	  =	np.random.permutation(self.token_count      )[: self.mask_count]
      a		:   List[str]	  =	np.zeros(self.token_count		,     dtype=__snake_case      )
      a		:   Any	  =	1

      a		:   List[str]	  =	mask.reshape((self.rand_size, self.rand_size)      )
      a		:   Tuple	  =	mask.repeat(self.scale		,     axis=0      ).repeat(self.scale		,     axis=1      )

      return torch.tensor(mask.flatten()      )
def  lowerCamelCase__			(		_A							):
  a		:   str	  =	torch.stack([example['pixel_values'] for example in examples]							)
  a		:   List[str]	  =	torch.stack([example['mask'] for example in examples]							)
  return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def  lowerCamelCase__			(		):
  # See all possible arguments in src/transformers/training_args.py
  # or by passing the --help flag to this script.
  # We now keep distinct sets of args, for a cleaner separation of concerns.

  a		:   int	  =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
  if len(sys.argv							) == 2 and sys.argv[1].endswith('.json'							):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    a  ,      a  ,      a		:   Dict	  =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
  else:
    a  ,      a  ,      a		:   int	  =	parser.parse_args_into_dataclasses()

  # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
  # information sent is the one passed as arguments along with your Python/PyTorch versions.
  send_example_telemetry('run_mim'					,     _A					,     _A							)

  # Setup logging
  logging.basicConfig(
      format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'					,     datefmt='%m/%d/%Y %H:%M:%S'					,     handlers=[logging.StreamHandler(sys.stdout							)]					,     )

  if training_args.should_log:
    # The default of training_args.log_level is passive, so we set log level at info here to have that default.
    transformers.utils.logging.set_verbosity_info()

  a		:   int	  =	training_args.get_process_log_level()
  logger.setLevel(_A							)
  transformers.utils.logging.set_verbosity(_A							)
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()

  # Log on each process the small summary:
  logger.warning(
      f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
      + f"""distributed training: {bool(training_args.local_rank != -1							)}, 16-bits training: {training_args.fpaa}"""							)
  logger.info(f"""Training/evaluation parameters {training_args}"""							)

  # Detecting last checkpoint.
  a		:   Tuple	  =	None
  if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
    a		:   List[Any]	  =	get_last_checkpoint(training_args.output_dir							)
    if last_checkpoint is None and len(os.listdir(training_args.output_dir							)							) > 0:
      raise ValueError(
          f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
          'Use --overwrite_output_dir to overcome.'							)
    elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
      logger.info(
          f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
          'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'							)

    # Initialize our dataset.
  a		:   Any	  =	load_dataset(
      data_args.dataset_name					,     data_args.dataset_config_name					,     data_files=data_args.data_files					,     cache_dir=model_args.cache_dir					,     use_auth_token=True if model_args.use_auth_token else None					,     )

  # If we don't have a validation split, split off a percentage of train as validation.
  a		:   Union[str, Any]	  =	None if 'validation' in ds.keys() else data_args.train_val_split
  if isinstance(data_args.train_val_split					,     _A							) and data_args.train_val_split > 0.0:
    a		:   Any	  =	ds['train'].train_test_split(data_args.train_val_split							)
    a		:   str	  =	split['train']
    a		:   Any	  =	split['test']

  # Create config
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  a		:   Tuple	  =	{
      'cache_dir': model_args.cache_dir,
      'revision': model_args.model_revision,
      'use_auth_token': True if model_args.use_auth_token else None,
  }
  if model_args.config_name_or_path:
    a		:   Any	  =	AutoConfig.from_pretrained(model_args.config_name_or_path					,     **_A							)
  elif model_args.model_name_or_path:
    a		:   Optional[int]	  =	AutoConfig.from_pretrained(model_args.model_name_or_path					,     **_A							)
  else:
    a		:   Optional[int]	  =	CONFIG_MAPPING[model_args.model_type]()
    logger.warning('You are instantiating a new config instance from scratch.'							)
    if model_args.config_overrides is not None:
      logger.info(f"""Overriding config: {model_args.config_overrides}"""							)
      config.update_from_string(model_args.config_overrides							)
      logger.info(f"""New config: {config}"""							)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
  if hasattr(_A					,     'decoder_type'							):
    a		:   List[str]	  =	'simmim'

  # adapt config
  a		:   str	  =	model_args.image_size if model_args.image_size is not None else config.image_size
  a		:   Union[str, Any]	  =	model_args.patch_size if model_args.patch_size is not None else config.patch_size
  a		:   Any	  =	(
      model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
  )

  config.update(
      {
          'image_size': model_args.image_size,
          'patch_size': model_args.patch_size,
          'encoder_stride': model_args.encoder_stride,
      }							)

  # create image processor
  if model_args.image_processor_name:
    a		:   Union[str, Any]	  =	AutoImageProcessor.from_pretrained(model_args.image_processor_name					,     **_A							)
  elif model_args.model_name_or_path:
    a		:   Union[str, Any]	  =	AutoImageProcessor.from_pretrained(model_args.model_name_or_path					,     **_A							)
  else:
    a		:   List[Any]	  =	{
        conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
    }
    a		:   int	  =	IMAGE_PROCESSOR_TYPES[model_args.model_type]()

  # create model
  if model_args.model_name_or_path:
    a		:   Tuple	  =	AutoModelForMaskedImageModeling.from_pretrained(
        model_args.model_name_or_path					,     from_tf=bool('.ckpt' in model_args.model_name_or_path							)					,     config=_A					,     cache_dir=model_args.cache_dir					,     revision=model_args.model_revision					,     use_auth_token=True if model_args.use_auth_token else None					,     )
  else:
    logger.info('Training new model from scratch'							)
    a		:   Union[str, Any]	  =	AutoModelForMaskedImageModeling.from_config(_A							)

  if training_args.do_train:
    a		:   Tuple	  =	ds['train'].column_names
  else:
    a		:   Dict	  =	ds['validation'].column_names

  if data_args.image_column_name is not None:
    a		:   Optional[Any]	  =	data_args.image_column_name
  elif "image" in column_names:
    a		:   str	  =	'image'
  elif "img" in column_names:
    a		:   Union[str, Any]	  =	'img'
  else:
    a		:   str	  =	column_names[0]

  # transformations as done in original SimMIM paper
  # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
  a		:   Optional[int]	  =	Compose(
      [
          Lambda(lambda _A							: img.convert('RGB'							) if img.mode != "RGB" else img							),
          RandomResizedCrop(model_args.image_size					,     scale=(0.67, 1.0)					,     ratio=(3.0 / 4.0, 4.0 / 3.0)							),
          RandomHorizontalFlip(),
          ToTensor(),
          Normalize(mean=image_processor.image_mean					,     std=image_processor.image_std							),
      ]							)

  # create mask generator
  a		:   Dict	  =	MaskGenerator(
      input_size=model_args.image_size					,     mask_patch_size=data_args.mask_patch_size					,     model_patch_size=model_args.patch_size					,     mask_ratio=data_args.mask_ratio					,     )

  def preprocess_images(_A							):
    a		:   Dict	  =	[transforms(_A							) for image in examples[image_column_name]]
    a		:   Optional[int]	  =	[mask_generator() for i in range(len(examples[image_column_name]							)							)]

    return examples

  if training_args.do_train:
    if "train" not in ds:
      raise ValueError('--do_train requires a train dataset'							)
    if data_args.max_train_samples is not None:
      a		:   List[Any]	  =	ds['train'].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
    # Set the training transforms
    ds["train"].set_transform(_A							)

  if training_args.do_eval:
    if "validation" not in ds:
      raise ValueError('--do_eval requires a validation dataset'							)
    if data_args.max_eval_samples is not None:
      a		:   str	  =	(
          ds['validation'].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
      )
    # Set the validation transforms
    ds["validation"].set_transform(_A							)

  # Initialize our trainer
  a		:   List[str]	  =	Trainer(
      model=_A					,     args=_A					,     train_dataset=ds['train'] if training_args.do_train else None					,     eval_dataset=ds['validation'] if training_args.do_eval else None					,     tokenizer=_A					,     data_collator=_A					,     )

  # Training
  if training_args.do_train:
    a		:   str	  =	None
    if training_args.resume_from_checkpoint is not None:
      a		:   Any	  =	training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      a		:   Dict	  =	last_checkpoint
    a		:   Optional[int]	  =	trainer.train(resume_from_checkpoint=_A							)
    trainer.save_model()
    trainer.log_metrics('train'					,     train_result.metrics							)
    trainer.save_metrics('train'					,     train_result.metrics							)
    trainer.save_state()

  # Evaluation
  if training_args.do_eval:
    a		:   List[Any]	  =	trainer.evaluate()
    trainer.log_metrics('eval'					,     _A							)
    trainer.save_metrics('eval'					,     _A							)

  # Write model card and (optionally) push to hub
  a		:   str	  =	{
      'finetuned_from': model_args.model_name_or_path,
      'tasks': 'masked-image-modeling',
      'dataset': data_args.dataset_name,
      'tags': ['masked-image-modeling'],
  }
  if training_args.push_to_hub:
    trainer.push_to_hub(**_A							)
  else:
    trainer.create_model_card(**_A							)


if __name__ == "__main__":
     main()